---
dataset:
- name: USSocialIssues_2016ElectionSurvey
tags:
  - survey
  - public-opinion
  - crime
  - welfare
  - united-states
  - election-2016
  - panel-data
license: mit
---


# Survey on Social Issues in the United States (2016 Election Study)

## Overview

This data product contains individual-level responses to an online survey experiment conducted in the run-up to—and immediately after—the 2016 U.S. presidential election by [Connor Jerzak](https://connorjerzak.com/), Rebecca Goldstein, and Yanilda María González.

Survey began on **12 September 2016** and continued through **mid-November 2016**, giving researchers a before/after snapshot of attitudes shaped by a highly salient national campaign.

**Key design features**

* **Crime-framing experiment.** Respondents read a mock police-blotter story with experimentally varied details (suspect race, number of break-ins, presence/absence of racial information) before answering questions about policy, crime perceptions, and social spending.  
* **Demographics & ideology.** Over 50 items capture party identification, vote choice, income, employment security, family status, education, racial identity, and core value trade-offs.  
* **Panel structure.** A subset of respondents was re-contacted after Election Day, enabling within-person analyses of opinion change.

## File Manifest

| File | Description |
|------|-------------|
| `survey_results.csv` | Clean, respondent-level dataset (wide format). Each column corresponds to a survey variable prefixed by its original Qualtrics question ID. |
| `Oct21_survey.pdf`  | Archived survey instrument, including consent form and full questionnaire. |

## Quick Start (R)

```r
library(tidyverse)
df <- read_csv("survey_results.csv")

# Recode experimental treatment
#   Q42 == "No" -> Control
#   Q42 == "Yes" & Q43 gives race
df <- df %>% 
  mutate(treat = case_when(
    Q42 == "No" ~ "Control",
    Q43 == "Black" ~ "Black",
    Q43 == "White" ~ "White"
  ))

# Estimate effect of racial cue on support for longer sentences
lm(long_sentences ~ treat + party_id + age, data = df)
```

## Variable Highlights

* **Safety perceptions:** `Q2`–`Q4`, `Q37`, `Q39`  
* **Crime policy preferences:** `Q11`, `Q12`  
* **Redistribution & welfare attitudes:** `Q8`, `Q9`, `Q46`–`Q51`  
* **2016 vote intention & choice:** `Q41`, `Q44`, `Q45`  
* **Economic security:** `Q29`–`Q32`  
* **Child-rearing values:** `Q33`–`Q36`

See `Oct21_survey.pdf` for exact wording and response options.

## Possible Use Cases

1. **Election-season opinion dynamics** – analyze the before/after panel to examine how campaign events (debates, the Comey letter, Election Day) shifted perceptions of crime, policing, or redistribution.  
2. **Stereotype activation & policy support** – estimate causal effects of suspect-race cues on punitive crime policies or welfare attitudes.  
3. **Replication exercises** – reproduce classic findings from ANES or GSS items using a contemporary MTurk sample; ideal for teaching regression, causal inference, or text analysis (e.g., coding open-ended crime causes in `Q10`).  
4. **Value trade-off scaling** – model latent moral or parenting value dimensions with the paired choice items (`Q33`–`Q36`).  
5. **Small-N machine-learning demos** – demonstrate text classification, topic modeling, or mixed-effects models on a manageable survey.

## Sampling & Fieldwork

Respondents were recruited via **Amazon Mechanical Turk**. Each wave paid \$0.25 and took ~5 minutes. The instrument included an informed-consent screen and was approved by the Harvard CUHS IRB. IP geo-coordinates (rounded to 3 decimals) were recorded for coarse location checks; no personally identifying information is included.

| Wave | Dates | N (unique) | Notes |
|------|-------|------------|-------|
| Pre-Election | 12 Sep – 04 Nov 2016 | 449 | Prior to Election Day |
| Post-Election | 09 Nov – 15 Nov 2016 | 546 | Post Election Dad |

## Data Quality Notes

* **Non-probability sample.** MTurk respondents skew younger, more educated, and more politically engaged than the general U.S. adult population.  
* **Attention checks.** Various items (e.g., number of break-ins retention check) facilitate quality screening.  
* **Missing values.** Skipped or invalid responses are coded `NA`.

